🔬 The Swap Lab

Our swap lab is designed so you can learn how to use professional tools without any risk! Customize your rules and watch your investment progress!

Laboratory Tools

The Laboratory is where theoretical knowledge meets practical experimentation. Each tool serves a specific purpose in helping you understand, test, and optimize DeFi strategies without risking real capital.

AMM Sandbox

What AMM stands for: Automated Market Maker - the algorithm that powers decentralized exchanges.

What the AMM Sandbox is: An interactive simulation environment where you can manipulate liquidity pools, execute trades, and observe how prices change according to the constant product formula.

The constant product formula:

x × y = k

Where:

• x = amount of token A in pool

• y = amount of token B in pool

• k = constant (doesn't change)

How to use the AMM Sandbox:

1. Access: Navigate to Laboratory → AMM Sandbox

2. Initialize pool:

   • Token A: Select first token (e.g., ETH)

   • Token A Amount: Set initial liquidity (e.g., 100 ETH)

   • Token B: Select second token (e.g., USDC)

   • Token B Amount: Set initial liquidity (e.g., 200,000 USDC)

   • Initial k: 100 × 200,000 = 20,000,000

3. Execute simulated trades:

   • Buy Token A: Enter amount of B to spend

   • Watch how A reserves decrease, B reserves increase

   • Observe price change

   • See how k remains constant

1. Analyze results:

   • Price impact percentage

   • New pool ratio

   • Slippage calculation

   • Arbitrage opportunities

Practical experiments:

Experiment 1: Understanding price impact

• Initial pool: 100 ETH / 200,000 USDC (price = $2000/ETH)

• Trade: Buy 10 ETH

• Calculation:

  • New ETH amount: 100 - 10 = 90 ETH

  • k must stay 20,000,000

  • New USDC amount: 20,000,000 / 90 = 222,222 USDC

  • USDC spent: 222,222 - 200,000 = 22,222 USDC

  • Effective price: 22,222 / 10 = $2,222/ETH

  • Price impact: 11.1%

• Learning: Larger trades relative to pool size = higher price impact

Experiment 2: Liquidity depth matters

• Pool A: 100 ETH / 200,000 USDC

• Pool B: 1000 ETH / 2,000,000 USDC

• Same trade: Buy 10 ETH in both pools

• Pool A: 11.1% price impact

• Pool B: ~1% price impact

• Learning: Deeper liquidity = less slippage

Experiment 3: Arbitrage opportunities

• External market: ETH = $2000

• AMM pool: ETH = $2100 (too expensive)

• Arbitrage action: Sell ETH to pool for profit

• Pool price drops toward $2000

• Learning: Arbitrageurs keep prices aligned

Real-world application:

• Before large trades, check liquidity depth

• Split large orders across multiple pools

• Use aggregators like 1inch that split orders automatically

• Understand your effective execution price

Why this matters:

• AMMs are THE innovation that makes DeFi possible

• Understanding this mechanism is fundamental to all DEX trading

• Helps you predict price impact before trading

• Essential for LP profitability analysis

Strategy Builder

What it is: An advanced tool for designing, backtesting, and optimizing DeFi trading strategies without coding.

Components of a strategy:

Entry conditions:

• Price crosses above/below moving average

• RSI oversold/overbought

• Volume spike

• Time-based (e.g., Monday mornings)

Exit conditions:

• Take profit at X% gain

• Stop loss at Y% loss

• Time-based exit

• Indicator-based exit

Position sizing:

• Fixed amount

• Percentage of portfolio

• Kelly Criterion

• Risk-adjusted sizing

How to build a strategy:

1. Navigate to Laboratory → Strategy Builder

2. Define strategy parameters:

   Basic Strategy Example: Mean Reversion

   • Asset: ETH/USDC

   • Timeframe: 1 hour

   • Entry Rule: Price is 5% below 20-period moving average

   • Exit Rule 1: Price returns to moving average (take profit)

   • Exit Rule 2: Price drops 10% from entry (stop loss)

   • Position Size: 10% of portfolio per trade

3. Backtest:

   • Historical Period: Select date range (e.g., last 6 months)

   • Initial Capital: Set starting amount ($10,000)

   • Run Backtest: Click button

4. Analyze results:

   • Total Return: +45%

   • Number of Trades: 23

   • Win Rate: 65%

   • Average Win: +8%

   • Average Loss: -4%

   • Max Drawdown: -15%

   • Sharpe Ratio: 1.8

   • Profit Factor: 2.2

Advanced strategies to test:

Momentum Strategy:

Entry:
- 50-day MA crosses above 200-day MA (Golden Cross)
- Volume is 2x above average
- RSI > 50

Exit:
- 50-day MA crosses below 200-day MA
- OR 15% stop loss
- OR 40% take profit

Position Size: 25% of portfolio

Grid Trading:

Setup:
- Define price range: $1800 - $2200
- Place 10 buy orders every $40 below current price
- Place 10 sell orders every $40 above current price
- Each order: 1% of portfolio

Rebalance:
- When buy order fills, place sell order above it
- When sell order fills, place buy order below it

Profit:
- Capture volatility in ranging markets

Dollar Cost Averaging (DCA):

Entry:
- Buy fixed amount every week
- Regardless of price

Exit:
- Hold for long term
- Only sell when target reached (e.g., 2x)

Benefits:
- Removes emotion
- Averages entry price
- Works in bull markets

Performance metrics explained:

Sharpe Ratio:

• Measures risk-adjusted returns

• Formula: (Return - Risk-free rate) / Standard Deviation

• 1.0 is good

• 2.0 is excellent

• 3.0 is exceptional

Max Drawdown:

• Largest peak-to-trough decline

• Measures worst-case scenario

• 20% drawdown = you must gain 25% to recover

• Keep below 30% for sustainability

Profit Factor:

• Gross profits / Gross losses

• 1.5 is acceptable

• 2.0 is good

• Shows if winners outweigh losers

Win Rate:

• Percentage of profitable trades

• Can be profitable with 40% win rate if wins are large

• High win rate doesn't guarantee profitability

Real-world application:

• Quantitative traders use similar tools (QuantConnect, TradingView Pine Script)

• Hedge funds backtest strategies extensively before deploying capital

• Retail traders can use TradingView strategy tester

• Never trust a strategy without proper backtesting

Limitations to understand:

• Past performance ≠ future results

• Backtesting can be curve-fit (over-optimized)

• Doesn't account for slippage, fees, or emotional factors

• Market conditions change (regime changes)

Liquidation Simulator

What it is: A tool for understanding and preparing for liquidation scenarios in leveraged positions.

Why liquidations happen: When you borrow funds for leverage, lenders need protection. If your collateral value drops below borrowed amount, the system liquidates your position to protect lenders.

The liquidation process:

1. Initial state:

   • Collateral: $10,000 ETH

   • Borrowed: $50,000 USDC

   • Leverage: 5x

   • Health Factor: 2.0 (safe)

2. Price drops:

   • ETH drops 30%

   • Collateral now: $7,000

   • Borrowed still: $50,000

   • Health Factor: 1.4 (warning)

3. Liquidation triggered:

   • ETH drops 40%

   • Collateral now: $6,000

   • Health Factor: 1.2 → Below 1.3 threshold

   • LIQUIDATED

4. Liquidation execution:

   • Protocol sells your $6,000 worth of ETH

   • Repays $50,000 debt (underwater position)

   • Liquidation penalty: ~5-10%

   • You lose everything

How to use the Liquidation Simulator:

1. Navigate to Laboratory → Liquidation Simulator

2. Enter position parameters:

   • Collateral Amount: $10,000

   • Collateral Asset: ETH (current price $2000)

   • Borrowed Amount: $40,000

   • Leverage: 5x

   • Liquidation Threshold: 130% (platform dependent)

3. Run simulation:

   • Scenario 1: ETH drops 10%

     • New collateral value: $9,000

     • Health factor: 1.4

     • Status: Safe

   • Scenario 2: ETH drops 20%

     • New collateral value: $8,000

     • Health factor: 1.25

     • Status: LIQUIDATION RISK

   • Scenario 3: ETH drops 25%

     • New collateral value: $7,500

     • Health factor: 1.15

     • Status: LIQUIDATED

4. Analyze results:

   • Liquidation price calculated

   • Safe zone identified

   • Recommended actions displayed

Liquidation price calculation:

Formula:

Liquidation Price = Entry Price × (1 - (1/Leverage) × Safety Margin)

Example:

• Entry: ETH at $2000

• Leverage: 5x

• Liquidation threshold: 1.3 (130%)

• Liquidation price = $2000 × (1 - (1/5) × 0.3) = $2000 × 0.94 = $1,880

This means a 6% drop liquidates your position with 5x leverage

Strategies to avoid liquidation:

1. Use lower leverage:

• 2x leverage = 50% drop needed for liquidation

• 5x leverage = 20% drop liquidates

• 10x leverage = 10% drop liquidates

2. Maintain collateral buffer:

• Don't max out your borrowing power

• Keep health factor above 2.0

• Leave room for volatility

3. Set up alerts:

• Alert at health factor 1.8

• Alert at health factor 1.5

• Alert at 10% from liquidation price

4. Have capital ready:

• Keep stablecoins available

• Add collateral if position weakens

• Don't lock all capital in positions

5. Use stop losses:

• Set stop loss before liquidation point

• Better to close with small loss than full liquidation

• Example: 5% stop loss vs 20% liquidation loss

Real-world liquidation examples:

Black Thursday (March 2020):

• ETH dropped 50% in one day

• $8M in liquidations on MakerDAO

• Gas fees spiked to 400+ gwei

• Some positions couldn't add collateral due to network congestion

May 2021 Crash:

• $10B in liquidations across all exchanges

• Bitcoin dropped from $60k to $30k

• Cascading liquidations (one liquidation triggers others)

Terra/LUNA Collapse (May 2022):

• $300M+ in liquidations

• Liquidation spirals accelerated collapse

• Some users lost millions

Real-world platforms and their liquidation parameters:

Aave:

• Liquidation threshold: 80-85% (varies by asset)

• Liquidation penalty: 5-10%

• Health factor calculation includes all assets

MakerDAO:

• Collateralization ratio: 150% minimum

• Liquidation penalty: 13%

• Auction system for liquidated collateral

Compound:

• Liquidation threshold: varies (typically 75%)

• Liquidation incentive: 8%

• Anyone can liquidate underwater positions

dYdX:

• Initial margin: 10% (10x leverage)

• Maintenance margin: 7.5%

• Automatic liquidation by protocol

MEV Calculator

What MEV stands for: Maximal Extractable Value (formerly Miner Extractable Value) - profit that can be extracted from manipulating transaction order in a block.

What it is: MEV is the invisible tax on DeFi transactions. Bots scan pending transactions (mempool), identify profitable opportunities, and front-run, back-run, or sandwich your trades.

Types of MEV:

1. Front-running:

• Bot sees your large buy order in mempool

• Bot submits same trade with higher gas fee

• Bot's transaction executes first

• Price increases

• Your trade executes at worse price

• Bot sells to you at inflated price

2. Back-running:

• Bot sees your trade that will impact price

• Bot submits transaction after yours

• Takes advantage of price change you created

• Example: You buy, price rises, bot sells

3. Sandwich attack:

• Bot sees your trade

• Bot front-runs (buys before you)

• Your trade executes (pushes price up)

• Bot back-runs (sells to you at high price)

• You get worst possible price

• Bot profits from both sides

4. Liquidation MEV:

• Bots monitor leveraged positions

• Detect positions near liquidation

• Trigger liquidations for bounty/profit

5. Arbitrage MEV:

• Price differences between DEXs

• Bots execute simultaneous trades

• Profit from price discrepancy

How to use the MEV Calculator:

1. Navigate to Laboratory → MEV Calculator

2. Input your trade details:

   • Token Pair: ETH/USDC

   • Trade Type: Buy

   • Amount: 10 ETH

   • Expected Price: $2000

   • Slippage Tolerance: 1%

   • Gas Price: Current network rate

   • Priority Fee: Additional tip for faster inclusion

3. Calculate MEV exposure:

   • Visibility Window: Time in mempool (typically 10-15 seconds)

   • MEV Bot Activity: Current network activity level

   • Potential Loss: Estimated sandwich attack impact

4. Results:

   • MEV Risk Score: High/Medium/Low

   • Estimated MEV Loss: $127 (1.27% of trade)

   • Protection Recommendations: Use Flashbots, set tighter slippage, split order

5. Simulate protection methods:

   • Without protection: Expected loss $127

   • With Flashbots Protect: Expected loss $0

   • With tighter slippage (0.3%): Expected loss $40

   • Split into 5 orders: Expected loss $30

     

Understanding the calculations:

Sandwich attack math: Assume your trade:

• Buy 10 ETH for USDC

• Pool has 1000 ETH / 2M USDC

• Your expected price: $2000

Bot's attack:

1. Bot front-runs: Buys 5 ETH

   • Takes price from $2000 to $2050

2. Your trade executes: Buy 10 ETH

   • You pay higher price: ~$2075 average

   • Price goes to $2100

3. Bot back-runs: Sells 5 ETH

   • Sells at ~$2090

   • Profit: $200 (5 ETH × $40 difference)

Your loss:

• Expected to pay: $20,000

• Actually paid: $20,750

• MEV loss: $750

Factors affecting MEV:

Trade size:

• Larger trades = more MEV opportunity

• $1M trade might lose 2-5% to MEV

• $1000 trade might lose 0.1%

Pool liquidity:

• Low liquidity pools = more MEV vulnerability

• High liquidity = less price impact = less MEV

Network congestion:

• High gas prices = more mempool time

• More time = more MEV opportunity

Token volatility:

• Volatile tokens = more arbitrage opportunities

• Stable pairs = less MEV

Protection strategies:

1. Use MEV-protected RPCs:

• Flashbots Protect: Transactions sent directly to miners

• Bypass mempool entirely

• No front-running possible

• Same gas cost

2. Private transactions:

• Submit to private mempools

• Miners include directly

• Services: Flashbots, Eden Network, BloXroute

3. Tighter slippage:

• Set 0.1-0.3% slippage

• Sandwich attack reverts if slippage exceeded

• Risk: transaction may fail if price moves

4. Split large orders:

• Instead of one 100 ETH trade

• Execute ten 10 ETH trades

• Reduces price impact

• Spreads over time

5. Use DEX aggregators:

• 1inch, Matcha, Cowswap

• Split orders across multiple pools

• Reduces price impact per pool

6. CoW Swap (Coincidence of Wants):

• Matches orders peer-to-peer first

• Only uses AMM if no match

• Gasless orders

• MEV protection built-in

Real-world MEV statistics:

MEV extracted (all-time):

• Over $680M extracted from Ethereum users

• $40M+ extracted in May 2021 alone

• Average user loses 0.5-1% of trade value

By strategy type:

• Sandwich attacks: ~$300M

• Arbitrage: ~$250M

• Liquidations: ~$130M

Who extracts MEV:

• Searchers: Bots scanning for opportunities

• Block builders: Construct optimal blocks

• Validators: Earn tips for including MEV bundles

Post-merge MEV landscape:

• MEV boost: 90%+ of validators use it

• Block builders specialize in MEV extraction

• More sophisticated strategies emerging

Real tools for MEV protection:

• Flashbots Protect RPC: Free, easy to add to MetaMask

• Cow Swap: Gasless trades with MEV protection

• 1inch Fusion: RFQ system bypasses AMMs

• Bloackroute: Enterprise-level MEV protection